tejadabheja/guru

webmind-brain-v1

A graph-based reasoning engine. Not a neural network. No gradient descent. No GPU required.

The brain learns by building a co-occurrence graph over word vectors, then reasons by converging through the graph. Every answer has a traceable source. Knowledge is editable and deletable.

Quick Start

pip install numpy fastapi uvicorn lmdb

from webmind import Brain

brain = Brain.from_pretrained("webmind/webmind-brain-v1")

# Teach it something
brain.teach("Paris is the capital of France")
brain.teach("London is the capital of England")

# Ask
result = brain.ask("capital of France")
print(result["answer"])      # paris capital france
print(result["confidence"])  # 0.85
print(result["strategy"])    # convergence / co-occurrence / abstain

# Generate fluent text
gen = brain.generate("Tell me about France", max_tokens=20, temperature=0.7)
print(gen["text"])

# Save
brain.flush()

OpenAI-Compatible Server

python serve.py
# Then:
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "capital of france"}]}'

Supports streaming ("stream": true), the /v1/models endpoint, and /health.

Architecture

Input -> Garbage Filter (heuristic + LSH)
      -> Tier 1: Q→A Direct Lookup (LRU + LMDB, <1ms)
      -> Tier 1.5: LSH Semantic Search (O(1) bucket lookup, seed concepts)
      -> Tier 2: Convergence Loop (multi-hop reasoning over sparse graph)
             -> Co-occurrence Search (complementary sparse signal)
             -> Sentence Retrieval (full text from LMDB)
      -> Confidence Floor (abstain if < 0.15)
      -> Web Search fallback (DuckDuckGo + Wikipedia)

Key properties:

• Co-occurrence graph: words that appear together pull toward each other in a sparse matrix
• Convergence loop: iteratively search the graph, blending discovered concepts back into the query until the output stabilizes
• Dual retrieval: dense neuron search + sparse co-occurrence search race in parallel
• Successor chains: each word neuron stores its top-10 successors for generation
• Confidence tracking: every neuron has a confidence score that grows when useful and shrinks when not
• LSH vocabulary filter: locality-sensitive hashing over MiniLM embeddings for garbage detection, morphological linking ("gravitational"→"gravity"), vocabulary dedup, and O(1) semantic search
• ScaNN backend: Google's anisotropic vector quantization for faster ANN search (optional, falls back to LSH)
• Int8 quantization: PolarQuant-inspired 4x embedding compression with ~1% accuracy loss
• Confidence floor: abstain rather than return weak convergence results (bad context > no context)
• Vocabulary pruning: score words by convergence contribution, remove low-value entries

What It Is Good At

• Factual Q&A with traceable sources
• Multi-hop reasoning (convergence crosses concept boundaries)
• Incremental learning (teach new facts at runtime, no retraining)
• Honest failure (says "I don't know" when it doesn't converge)
• Knowledge editing (delete a neuron = delete a fact)

What It Is Not Good At

• Fluent prose generation (output is concept-oriented, not grammatically polished)
• Creative writing
• Long-form text
• Tasks requiring deep syntactic understanding

Training Data

This model ships empty. It learns from what you teach it. The from_pretrained download includes the graph structure and vocabulary but no pre-loaded knowledge.

For evaluation, we tested on HotPotQA (200 train, 50 test) achieving 72% exact match with word neurons + successor chains.

Limitations

• Context window is limited by the convergence loop (not fixed-length, but practically ~10 hops)
• Generation quality depends heavily on what has been taught
• No coreference resolution beyond what convergence provides
• Function words are stripped during reasoning (grammar handled separately)

Citation

If you use this work, please cite:

@software{webmind_brain_2026,
  title={Webmind Brain: Graph-Based Reasoning Without Neural Networks},
  url={https://github.com/webmind-ai/webmind-brain},
  year={2026},
  license={Apache-2.0}
}

License

Apache 2.0